RNNCell#

class brainpy.dyn.RNNCell(num_in, num_out, state_initializer=ZeroInit, Wi_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[ 479025946 4206239744]), Wh_initializer=XavierNormal(scale=1.0, mode=fan_avg, in_axis=-2, out_axis=-1, distribution=truncated_normal, rng=[ 479025946 4206239744]), b_initializer=ZeroInit, activation='relu', mode=None, train_state=False, name=None)[source]#

Basic fully-connected RNN core.

Given \(x_t\) and the previous hidden state \(h_{t-1}\) the core computes

\[h_t = \mathrm{ReLU}(w_i x_t + b_i + w_h h_{t-1} + b_h)\]

The output is equal to the new state, \(h_t\).

Parameters:

  • num_in (int) – The dimension of the input vector

  • num_out (int) – The number of hidden unit in the node.

  • state_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The state initializer.

  • Wi_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The input weight initializer.

  • Wh_initializer (callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The hidden weight initializer.

  • b_initializer (optional, callable, Initializer, bm.ndarray, jax.numpy.ndarray) – The bias weight initializer.

  • activation (str, callable) – The activation function. It can be a string or a callable function. See brainpy.math.activations for more details.

reset_state(batch_or_mode=None, **kwargs)[source]#

Reset function which resets local states in this model.

Simply speaking, this function should implement the logic of resetting of local variables in this node.

See https://brainpy.readthedocs.io/en/latest/tutorial_toolbox/state_resetting.html for details.

update(x)[source]#

The function to specify the updating rule.